{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date Features from the datetime variable\n",
    "\n",
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "Time series data are, by definition, time-indexed. The \"time\" component has information about the date and time. We can extract a number of features from the time component of the index.\n",
    "\n",
    "In this notebook, we will see how we can easily derive many date-related features.\n",
    "\n",
    "\n",
    "## Features from the date part:\n",
    "\n",
    "Below are some of the features that we can extract from the date part of the datetime variable off-the-shelf using [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-date-components):\n",
    "\n",
    "\n",
    "- pandas.Series.dt.year\n",
    "- pandas.Series.dt.quarter\n",
    "- pandas.Series.dt.month\n",
    "- pandas.Series.dt.isocalendar().week\n",
    "- pandas.Series.dt.day\n",
    "- pandas.Series.dt.day_of_week\n",
    "- pandas.Series.dt.weekday\n",
    "- pandas.Series.dt.dayofyear\n",
    "- pandas.Series.dt.day_of_year\n",
    "\n",
    "- pandas.Series.dt.is_month_start\n",
    "- pandas.Series.dt.is_month_end\n",
    "- pandas.Series.dt.is_quarter_start\n",
    "- pandas.Series.dt.is_quarter_end\n",
    "- pandas.Series.dt.is_year_start\n",
    "- pandas.Series.dt.is_year_end\n",
    "- pandas.Series.dt.is_leap_year\n",
    "- pandas.Series.dt.days_in_month\n",
    "\n",
    "We can use the features obtained with pandas to create even more features, such as:\n",
    "\n",
    "- Semester\n",
    "- Is Weekend?\n",
    "\n",
    "And if we know the dates representing bank holidays in the countries of interest, we could create functions to add those as well.\n",
    "\n",
    "\n",
    "\n",
    "## The dataset\n",
    "\n",
    "We will use the Online Retail II Data Set available in the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/machine-learning-databases/00502/).\n",
    "\n",
    "Download the xlsx file from the link above and save it in the **Datasets** folder within this repo.\n",
    "\n",
    "**Citation**:\n",
    "\n",
    "Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "\n",
    "## In this demo\n",
    "\n",
    "We will extract different features from the datetime variable: **InvoiceDate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1067371, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country  \n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File path:\n",
    "file = \"../Datasets/online_retail_II.xlsx\"\n",
    "\n",
    "# The data is provided as two sheets in a single Excel file.\n",
    "# Each sheet contains a different time period.\n",
    "# Load both and join them into a single dataframe\n",
    "# as shown below:\n",
    "\n",
    "df_1 = pd.read_excel(file, sheet_name=\"Year 2009-2010\")\n",
    "df_2 = pd.read_excel(file, sheet_name=\"Year 2010-2011\")\n",
    "\n",
    "data = pd.concat([df_1, df_2])\n",
    "\n",
    "print(data.shape)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset, we have the datetime variable in a column called InvoiceDate. We could also have it in the dataframe index. The procedure for extracting the date and time features is identical. That is, we would use the methods from pandas dt as shown below.\n",
    "\n",
    "The dataset contains sales information for different customers in different countries. Customers may have made one or multiple purchases from the business that provided the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's determine the type of data in the datetime variable.\n",
    "\n",
    "data[\"InvoiceDate\"].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset, the variable is already parsed as datetime data.\n",
    "\n",
    "In some datasets, the datetime variable may be cast as an object, i.e., strings. In these cases, before carrying on with the rest of the notebook, we should re-cast it from object into datetime, as we do in the following cell: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>InvoiceDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date         InvoiceDate\n",
       "0 2009-12-01 07:45:00 2009-12-01 07:45:00\n",
       "1 2009-12-01 07:45:00 2009-12-01 07:45:00\n",
       "2 2009-12-01 07:45:00 2009-12-01 07:45:00\n",
       "3 2009-12-01 07:45:00 2009-12-01 07:45:00\n",
       "4 2009-12-01 07:45:00 2009-12-01 07:45:00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is how we parse date strings into datetime format:\n",
    "\n",
    "data[\"date\"] = pd.to_datetime(data[\"InvoiceDate\"])\n",
    "\n",
    "data[[\"date\", \"InvoiceDate\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date           datetime64[ns]\n",
       "InvoiceDate    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's inspect the new data type.\n",
    "\n",
    "data[[\"date\", \"InvoiceDate\"]].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date and time parts\n",
    "\n",
    "The date and time parts of the datetime variable are not useful for predictions, but they may be handy for data analysis and grouping. \n",
    "\n",
    "Here we show how we can extract the date and time parts of the datetime variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2009-12-01\n",
       "1    2009-12-01\n",
       "2    2009-12-01\n",
       "3    2009-12-01\n",
       "4    2009-12-01\n",
       "Name: date_part, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the date part.\n",
    "\n",
    "data[\"date_part\"] = data[\"date\"].dt.date\n",
    "\n",
    "data[\"date_part\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    07:45:00\n",
       "1    07:45:00\n",
       "2    07:45:00\n",
       "3    07:45:00\n",
       "4    07:45:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the time part.\n",
    "# (we don't need it for this demo,\n",
    "# so I will not add it to the dataframe).\n",
    "\n",
    "time_ = data[\"date\"].dt.time\n",
    "\n",
    "time_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features derived from date\n",
    "\n",
    "In this notebook, we will focus on extracting features from the **date part** of the datetime variable. We can derive these features from the original datetime variable or the date part of the datetime variable. \n",
    "\n",
    "###  Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   invoice_year                date\n",
       "0          2009 2009-12-01 07:45:00\n",
       "1          2009 2009-12-01 07:45:00\n",
       "2          2009 2009-12-01 07:45:00\n",
       "3          2009 2009-12-01 07:45:00\n",
       "4          2009 2009-12-01 07:45:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract year.\n",
    "\n",
    "data[\"invoice_year\"] = data[\"date\"].dt.year\n",
    "\n",
    "data[[\"invoice_year\", \"date\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2009, 2010, 2011], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unique values in the new variable.\n",
    "\n",
    "data[\"invoice_year\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year start and end\n",
    "\n",
    "Let's create a feature that flags if the date corresponds to the first or last day of the year. Pandas will return a True/False feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_year_start</th>\n",
       "      <th>invoice_year_end</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   invoice_year_start  invoice_year_end                date\n",
       "0               False             False 2009-12-01 07:45:00\n",
       "1               False             False 2009-12-01 07:45:00\n",
       "2               False             False 2009-12-01 07:45:00\n",
       "3               False             False 2009-12-01 07:45:00\n",
       "4               False             False 2009-12-01 07:45:00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract year start and year end.\n",
    "\n",
    "data[\"invoice_year_start\"] = data[\"date\"].dt.is_year_start\n",
    "data[\"invoice_year_end\"] = data[\"date\"].dt.is_year_end\n",
    "\n",
    "data[[\"invoice_year_start\", \"invoice_year_end\", \"date\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_year_start\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_year_end\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No sales over the first and last day of the year. Makes sense, those are bank holidays in many countries, including the UK, were most of the sales were done in this dataset.\n",
    "\n",
    "### Leap year\n",
    "\n",
    "Let's create a feature that flags if a year was a leap year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>invoice_year_leap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   invoice_year  invoice_year_leap\n",
       "0          2009              False\n",
       "1          2009              False\n",
       "2          2009              False\n",
       "3          2009              False\n",
       "4          2009              False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract leap year.\n",
    "\n",
    "data[\"invoice_year_leap\"] = data[\"date\"].dt.is_leap_year\n",
    "\n",
    "data[[\"invoice_year\", \"invoice_year_leap\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_year_leap\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "None of 2009, 2010, 2011 were leap years.\n",
    "\n",
    "### Extract quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_quarter\n",
       "0 2009-12-01 07:45:00                4\n",
       "1 2009-12-01 07:45:00                4\n",
       "2 2009-12-01 07:45:00                4\n",
       "3 2009-12-01 07:45:00                4\n",
       "4 2009-12-01 07:45:00                4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract quarter from date variable - takes values 1 to 4.\n",
    "\n",
    "data[\"invoice_quarter\"] = data[\"date\"].dt.quarter\n",
    "\n",
    "data[[\"date\", \"invoice_quarter\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_quarter\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quarter start and end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_quarter_start</th>\n",
       "      <th>invoice_quarter_end</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   invoice_quarter_start  invoice_quarter_end                date\n",
       "0                  False                False 2009-12-01 07:45:00\n",
       "1                  False                False 2009-12-01 07:45:00\n",
       "2                  False                False 2009-12-01 07:45:00\n",
       "3                  False                False 2009-12-01 07:45:00\n",
       "4                  False                False 2009-12-01 07:45:00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract quarter start and end.\n",
    "\n",
    "data[\"invoice_quarter_start\"] = data[\"date\"].dt.is_quarter_start\n",
    "data[\"invoice_quarter_end\"] = data[\"date\"].dt.is_quarter_end\n",
    "\n",
    "data[[\"invoice_quarter_start\", \"invoice_quarter_end\", \"date\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_quarter_start\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>date</th>\n",
       "      <th>date_part</th>\n",
       "      <th>invoice_year</th>\n",
       "      <th>invoice_year_start</th>\n",
       "      <th>invoice_year_end</th>\n",
       "      <th>invoice_year_leap</th>\n",
       "      <th>invoice_quarter</th>\n",
       "      <th>invoice_quarter_start</th>\n",
       "      <th>invoice_quarter_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147682</th>\n",
       "      <td>503430</td>\n",
       "      <td>21523</td>\n",
       "      <td>DOOR MAT FANCY FONT HOME SWEET HOME</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147683</th>\n",
       "      <td>503430</td>\n",
       "      <td>21524</td>\n",
       "      <td>DOOR MAT SPOTTY HOME SWEET HOME</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147684</th>\n",
       "      <td>503430</td>\n",
       "      <td>48116</td>\n",
       "      <td>DOOR MAT MULTICOLOUR STRIPE</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147685</th>\n",
       "      <td>503430</td>\n",
       "      <td>48129</td>\n",
       "      <td>DOOR MAT TOPIARY</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147686</th>\n",
       "      <td>503430</td>\n",
       "      <td>48187</td>\n",
       "      <td>DOOR MAT NEW ENGLAND</td>\n",
       "      <td>10</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>13361.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010-04-01 07:49:00</td>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Invoice StockCode                          Description  Quantity  \\\n",
       "147682  503430     21523  DOOR MAT FANCY FONT HOME SWEET HOME        10   \n",
       "147683  503430     21524      DOOR MAT SPOTTY HOME SWEET HOME        10   \n",
       "147684  503430     48116          DOOR MAT MULTICOLOUR STRIPE        10   \n",
       "147685  503430     48129                     DOOR MAT TOPIARY        10   \n",
       "147686  503430     48187                 DOOR MAT NEW ENGLAND        10   \n",
       "\n",
       "               InvoiceDate  Price  Customer ID         Country  \\\n",
       "147682 2010-04-01 07:49:00   6.35      13361.0  United Kingdom   \n",
       "147683 2010-04-01 07:49:00   6.35      13361.0  United Kingdom   \n",
       "147684 2010-04-01 07:49:00   6.35      13361.0  United Kingdom   \n",
       "147685 2010-04-01 07:49:00   6.35      13361.0  United Kingdom   \n",
       "147686 2010-04-01 07:49:00   6.35      13361.0  United Kingdom   \n",
       "\n",
       "                      date   date_part  invoice_year  invoice_year_start  \\\n",
       "147682 2010-04-01 07:49:00  2010-04-01          2010               False   \n",
       "147683 2010-04-01 07:49:00  2010-04-01          2010               False   \n",
       "147684 2010-04-01 07:49:00  2010-04-01          2010               False   \n",
       "147685 2010-04-01 07:49:00  2010-04-01          2010               False   \n",
       "147686 2010-04-01 07:49:00  2010-04-01          2010               False   \n",
       "\n",
       "        invoice_year_end  invoice_year_leap  invoice_quarter  \\\n",
       "147682             False              False                2   \n",
       "147683             False              False                2   \n",
       "147684             False              False                2   \n",
       "147685             False              False                2   \n",
       "147686             False              False                2   \n",
       "\n",
       "        invoice_quarter_start  invoice_quarter_end  \n",
       "147682                   True                False  \n",
       "147683                   True                False  \n",
       "147684                   True                False  \n",
       "147685                   True                False  \n",
       "147686                   True                False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With this command we can see that the variable we created coincides\n",
    "# with the quarter start - just a sanity check.\n",
    "\n",
    "data[data[\"invoice_quarter_start\"] == True].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract semester\n",
    "\n",
    "With the quarter information, we can obtain the semester."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_semester</th>\n",
       "      <th>invoice_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   invoice_semester  invoice_quarter\n",
       "0                 2                4\n",
       "1                 2                4\n",
       "2                 2                4\n",
       "3                 2                4\n",
       "4                 2                4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract semester.\n",
    "\n",
    "data[\"invoice_semester\"] = np.where(data[\"invoice_quarter\"].isin([1, 2]), 1, 2)\n",
    "\n",
    "data[[\"invoice_semester\", \"invoice_quarter\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_semester\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_month\n",
       "0 2009-12-01 07:45:00             12\n",
       "1 2009-12-01 07:45:00             12\n",
       "2 2009-12-01 07:45:00             12\n",
       "3 2009-12-01 07:45:00             12\n",
       "4 2009-12-01 07:45:00             12"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract month - 1 to 12.\n",
    "\n",
    "data[\"invoice_month\"] = data[\"date\"].dt.month\n",
    "\n",
    "data[[\"date\", \"invoice_month\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_month\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Days in month\n",
    "\n",
    "Useful for adjusting the time series based on days. \n",
    "\n",
    "If we are forecasting sales per month, we may need this adjustment, as longer months may incur in more sales, just because there are more days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_in_month</th>\n",
       "      <th>invoice_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   days_in_month  invoice_month\n",
       "0             31             12\n",
       "1             31             12\n",
       "2             31             12\n",
       "3             31             12\n",
       "4             31             12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of days in a month.\n",
    "\n",
    "data[\"days_in_month\"] = data[\"date\"].dt.days_in_month\n",
    "\n",
    "data[[\"days_in_month\", \"invoice_month\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 28, 30], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"days_in_month\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Month start and end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract month start and end.\n",
    "\n",
    "data[\"invoice_month_start\"] = data[\"date\"].dt.is_month_start\n",
    "data[\"invoice_month_end\"] = data[\"date\"].dt.is_month_end\n",
    "\n",
    "data[\"invoice_month_start\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract week of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_week\n",
       "0 2009-12-01 07:45:00            49\n",
       "1 2009-12-01 07:45:00            49\n",
       "2 2009-12-01 07:45:00            49\n",
       "3 2009-12-01 07:45:00            49\n",
       "4 2009-12-01 07:45:00            49"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract week of the year - varies from 1 to 52.\n",
    "\n",
    "data[\"invoice_week\"] = data[\"date\"].dt.isocalendar().week\n",
    "\n",
    "data[[\"date\", \"invoice_week\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IntegerArray>\n",
       "[49, 50, 51, 52,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
       " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]\n",
       "Length: 52, dtype: UInt32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_week\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract days in various formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_day\n",
       "0 2009-12-01 07:45:00            1\n",
       "1 2009-12-01 07:45:00            1\n",
       "2 2009-12-01 07:45:00            1\n",
       "3 2009-12-01 07:45:00            1\n",
       "4 2009-12-01 07:45:00            1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Day of the month - numeric from 1-31.\n",
    "\n",
    "data[\"invoice_day\"] = data[\"date\"].dt.day\n",
    "\n",
    "data[[\"date\", \"invoice_day\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 13, 14, 15, 16, 17, 18,\n",
       "       20, 21, 22, 23, 12, 19, 24, 25, 26, 27, 28, 29, 31, 30],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_dayofweek\n",
       "0 2009-12-01 07:45:00                  1\n",
       "1 2009-12-01 07:45:00                  1\n",
       "2 2009-12-01 07:45:00                  1\n",
       "3 2009-12-01 07:45:00                  1\n",
       "4 2009-12-01 07:45:00                  1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Day of the week - from 0 to 6.\n",
    "\n",
    "# It is assumed the week starts on Monday,\n",
    "# denoted by 0, and ends on Sunday, denoted by 6.\n",
    "\n",
    "data[\"invoice_dayofweek\"] = data[\"date\"].dt.dayofweek\n",
    "\n",
    "data[[\"date\", \"invoice_dayofweek\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_dayofweek\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_day_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date invoice_day_name\n",
       "0 2009-12-01 07:45:00          Tuesday\n",
       "1 2009-12-01 07:45:00          Tuesday\n",
       "2 2009-12-01 07:45:00          Tuesday\n",
       "3 2009-12-01 07:45:00          Tuesday\n",
       "4 2009-12-01 07:45:00          Tuesday"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Day of the week - string (not useful for predictions,\n",
    "# but since we are here...).\n",
    "\n",
    "data[\"invoice_day_name\"] = data[\"date\"].dt.day_name()\n",
    "\n",
    "data[[\"date\", \"invoice_day_name\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday',\n",
       "       'Monday'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_day_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_day_name</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date invoice_day_name  is_weekend\n",
       "0 2009-12-01 07:45:00          Tuesday       False\n",
       "1 2009-12-01 07:45:00          Tuesday       False\n",
       "2 2009-12-01 07:45:00          Tuesday       False\n",
       "3 2009-12-01 07:45:00          Tuesday       False\n",
       "4 2009-12-01 07:45:00          Tuesday       False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Was the invoice issued on a weekend?\n",
    "\n",
    "data[\"is_weekend\"] = data[\"date\"].dt.dayofweek > 4\n",
    "\n",
    "data[[\"date\", \"invoice_day_name\", \"is_weekend\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"is_weekend\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>invoice_day_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  invoice_day_year\n",
       "0 2009-12-01 07:45:00               335\n",
       "1 2009-12-01 07:45:00               335\n",
       "2 2009-12-01 07:45:00               335\n",
       "3 2009-12-01 07:45:00               335\n",
       "4 2009-12-01 07:45:00               335"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Day of the year - 1 to 365.\n",
    "\n",
    "# I can't imagine when this feature would be\n",
    "# useful. Maybe, if we had data for several years,\n",
    "# to identify some repetitive pattern.\n",
    "\n",
    "data[\"invoice_day_year\"] = data[\"date\"].dt.dayofyear\n",
    "\n",
    "data[[\"date\", \"invoice_day_year\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min      4\n",
       "max    357\n",
       "Name: invoice_day_year, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"invoice_day_year\"].agg([\"min\", \"max\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "\n",
    "We can use these features as input features to a regression model. And we can use many of these features to try and understand more about our time series data as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, I will drop data from unknown customers.\n",
    "# In other words, when the customer's ID is\n",
    "# missing.\n",
    "\n",
    "data.dropna(subset=[\"Customer ID\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Invoice</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>Price</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>date</th>\n",
       "      <th>date_part</th>\n",
       "      <th>...</th>\n",
       "      <th>invoice_month</th>\n",
       "      <th>days_in_month</th>\n",
       "      <th>invoice_month_start</th>\n",
       "      <th>invoice_month_end</th>\n",
       "      <th>invoice_week</th>\n",
       "      <th>invoice_day</th>\n",
       "      <th>invoice_dayofweek</th>\n",
       "      <th>invoice_day_name</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>invoice_day_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>False</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice StockCode                          Description  Quantity  \\\n",
       "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
       "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  Price  Customer ID         Country                date  \\\n",
       "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom 2009-12-01 07:45:00   \n",
       "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom 2009-12-01 07:45:00   \n",
       "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom 2009-12-01 07:45:00   \n",
       "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom 2009-12-01 07:45:00   \n",
       "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom 2009-12-01 07:45:00   \n",
       "\n",
       "    date_part  ...  invoice_month  days_in_month  invoice_month_start  \\\n",
       "0  2009-12-01  ...             12             31                 True   \n",
       "1  2009-12-01  ...             12             31                 True   \n",
       "2  2009-12-01  ...             12             31                 True   \n",
       "3  2009-12-01  ...             12             31                 True   \n",
       "4  2009-12-01  ...             12             31                 True   \n",
       "\n",
       "   invoice_month_end  invoice_week  invoice_day  invoice_dayofweek  \\\n",
       "0              False            49            1                  1   \n",
       "1              False            49            1                  1   \n",
       "2              False            49            1                  1   \n",
       "3              False            49            1                  1   \n",
       "4              False            49            1                  1   \n",
       "\n",
       "   invoice_day_name  is_weekend  invoice_day_year  \n",
       "0           Tuesday       False               335  \n",
       "1           Tuesday       False               335  \n",
       "2           Tuesday       False               335  \n",
       "3           Tuesday       False               335  \n",
       "4           Tuesday       False               335  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove orders that were cancelled.\n",
    "\n",
    "cancelled = data[\"Invoice\"].apply(lambda x: str(x)[0] == \"C\")\n",
    "data = data[~cancelled]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Items Sold in the UK')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the time series sales for the United Kingdom.\n",
    "\n",
    "data.groupby([\"date_part\", \"Country\"])[\"Quantity\"].sum().unstack()[\n",
    "    \"United Kingdom\"\n",
    "].plot(figsize=(18, 4))\n",
    "plt.ylabel(\"Items sold\")\n",
    "plt.title(\"Items Sold in the UK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_weekend</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>594972</td>\n",
       "      <td>7786020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>130324</td>\n",
       "      <td>932803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count      sum\n",
       "is_weekend                 \n",
       "False       594972  7786020\n",
       "True        130324   932803"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Narrow down the data to the UK.\n",
    "\n",
    "uk = data[data[\"Country\"] == \"United Kingdom\"]\n",
    "\n",
    "# Do we sell more items on weekends?\n",
    "\n",
    "# With sum we find the total number of items sold.\n",
    "# With count we find the total number of sales made.\n",
    "\n",
    "uk.groupby(\"is_weekend\")[\"Quantity\"].agg([\"count\", \"sum\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean_no_items_per_sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_weekend</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>594972</td>\n",
       "      <td>7786020</td>\n",
       "      <td>13.086364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>130324</td>\n",
       "      <td>932803</td>\n",
       "      <td>7.157569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count      sum  mean_no_items_per_sale\n",
       "is_weekend                                         \n",
       "False       594972  7786020               13.086364\n",
       "True        130324   932803                7.157569"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many items we sell per individual sale:\n",
    "\n",
    "temp = uk.groupby(\"is_weekend\")[\"Quantity\"].agg([\"count\", \"sum\"])\n",
    "temp[\"mean_no_items_per_sale\"] = temp[\"sum\"] / temp[\"count\"]\n",
    "\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we sell more items during the week. For every individual sale we make during the week, we sell 13 items, whereas on weekends we sell 7 items.\n",
    "\n",
    "Maybe it is because on Sunday the retail company is closed. Then, we see half the sales of a normal day. \n",
    "\n",
    "**Would you like to go ahead and have a look at that? As homework :)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Items Sold in the UK')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many items do we sell throughout the year?\n",
    "\n",
    "uk.groupby([\"invoice_quarter\", \"invoice_year\"])[\"Quantity\"].sum().unstack().plot(\n",
    "    xticks=[1, 2, 3, 4]\n",
    ")\n",
    "plt.ylabel(\"Items sold\")\n",
    "plt.title(\"Items Sold in the UK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Items Sold in the UK')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's try and see if there is seasonality.\n",
    "\n",
    "uk.groupby([\"invoice_year\", \"invoice_quarter\"])[\"Quantity\"].sum().plot(rot=45)\n",
    "plt.ylabel(\"Items sold\")\n",
    "plt.title(\"Items Sold in the UK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is hard to say for sure. We would need more data, but there seems to be quarterly seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Items Sold in the UK')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# If we go down to months instead of quarters...\n",
    "\n",
    "uk.groupby([\"invoice_month\", \"invoice_year\"])[\"Quantity\"].sum().unstack().plot()\n",
    "plt.ylabel(\"Items sold\")\n",
    "plt.title(\"Items Sold in the UK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be seasonality, with peak sales occurring from September to December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Items Sold in the UK')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And since we are here, let's look at weekly data.\n",
    "\n",
    "uk.groupby([\"invoice_week\", \"invoice_year\"])[\"Quantity\"].sum().unstack().plot(\n",
    "    figsize=(12, 4)\n",
    ")\n",
    "plt.ylabel(\"Items sold\")\n",
    "plt.title(\"Items Sold in the UK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some thoughts\n",
    "\n",
    "In this notebook, we presented a lot of features that can be extracted from the date part of a datetime variable. Some of these features will be suitable for analysis, and some could be suitable for forecasting.\n",
    "\n",
    "**Which features should we create?**\n",
    "\n",
    "It will depend on what we are trying to forecast. If we are trying to make weekly forecasts, then week features might be the most useful.\n",
    "\n",
    "Data exploration together with defining the forecasting horizon framework will help us decide which features are the most useful.\n",
    "\n",
    "If we want to automate feature engineering, we may as well extract as many features as possible, and then let algorithms decide which ones are useful.\n",
    "\n",
    "In fact, there are some libraries that output date features automatically, which we will explore in coming notebooks.\n",
    "\n",
    "That is all for this one!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "fets",
   "language": "python",
   "name": "fets"
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    "name": "ipython",
    "version": 3
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": "block",
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  }
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 "nbformat": 4,
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